Patient signal analysis based on vector analysis

ABSTRACT

Disclosed herein is a framework for facilitating patient signal analysis based on vector analysis. In accordance with one aspect, a set of vectors is generated from a patient signal data waveform. The vectors may be directed from a common center to points of interest on the patient signal data waveform. The framework may further extract one or more vector parameters from the set of vectors, and determine one or more vector ratios based on the vector parameters to monitor changes in the patient signal data waveform.

CROSS-REFERENCE TO RELATED APPLICATION

This patent application is a divisional and claims the benefit ofpriority under 35 USC § 120 to pending U.S. application Ser. No.14/541,619, filed on Nov. 14, 2014.

TECHNICAL FIELD

The present disclosure generally relates to systems and methods foranalyzing and characterizing patient signals

BACKGROUND

Coronary artery disease (CAD) is one of the most common causes of deathin the United States, accounting for nearly 500,000 deaths each year.Studies estimate that 50% of men and 33% of women under the age of 40will develop some form of CAD sometime during their lifetime. Suddencardiac death has steadily accounted for approximately 50% of allheart-related, out-of-hospital deaths and improved clinical proceduresalmost entirely ignore this group. The fact that patients generally failto recognize their symptoms and seek prompt medical attentioncontributes to these tragic statistics. The principal manifestations ofCAD are coronary artherosclerosis (hardening of coronary arteries) orstenosis (narrowing of arteries), both of which ultimately force areduction of the coronary circulation (myocardial ischemia, infarction,or other kind of cardiac arrhythmia). However, the early stages of CADare usually non-symptomatic and invisible with current clinical cardiacsignal analysis strategies.

Early arrhythmia recognition is critical for rhythm management ofcardiac disorders. Currently, signal waveform and time domain parameteranalysis of cardiac cycle depolarization and repolarization, such as Pwave, QRS complex, ST segment, T wave, are used for cardiac arrhythmiamonitoring and identification. However, such traditional clinicalmethodologies are sometimes subjective and time-consuming, requiring theuser to possess expertise and clinical experience to achieve accurateinterpretation and proper cardiac rhythm management.

Traditional medical methods usually focus on time domain analysis (e.g.,amplitude, latency, etc.) or frequency domain analysis (e.g., power,spectrum, etc.), which may not accurately capture minute signal changesin the partial signal portion (e.g., P wave, QRS complex, ST segment,etc.) of cardiac activities. Such signal changes are usually invisiblein signal wave morphology display or require extensive clinicalexpertise to achieve accurate diagnosis. Consequently, it may result inhigh failure rate of arrhythmia diagnosis and high number of falsealarms. For example, a false negative (FN) results when the screeningtest wrongly makes the decision that a subject does not have disease X(e.g., myocardial ischemia) when he or she does in fact have thedisease. These concerns raise a need for a new approach to preciselyextract arrhythmia pathology information with high reliability andsensitivity from ongoing cardiac signals, which can diagnose partialsignal portions of heart tissues.

Further, traditional methods based on voltage amplitude changes andvariation analysis may not be sufficient for cardiac function evaluationand pathology diagnosis, especially since there is no quantitative linkbetween the myocardial ischemia event/status and the amplitude andvariation index. Known clinical diagnosis of myocardial ischemia (MI)and detection of infarction are based on the gold clinical standardbased on ST segment voltage deviation (e.g., 0.1 mV elevation formyocardial ischemia detection). However, there are at least twoshortcomings with this gold standard: (a) this standard only works forsurface ECG signals, but not for intra-cardiac electrogram (ICEG)signals; (b) ST segment deviation (voltage) cannot be utilized as aquantitative method for myocardial ischemia severity diagnosis andcharacterization.

Current methods may not be able to qualitatively and quantitativelycapture or characterize minute signal changes and predict thepathological trend, especially in the early stage of tissuemalfunctioning and acute myocardial ischemia. Known methods may notefficiently analyze and predict the real-time growing trend of cardiacarrhythmias, such as the pathology trend from low risk to medium risk,and then to high risk (i.e., severe and fatal) rhythm, especially forlife threatening arrhythmia, such as ventricular tachycardia (VT).

SUMMARY

The present disclosure relates to a framework for facilitating patientsignal analysis based on vector analysis. In accordance with one aspect,a set of vectors is generated from a patient signal data waveform. Thevectors may be directed from a common center to points of interest onthe patient signal data waveform. The framework may further extract oneor more vector parameters from the set of vectors, and determine one ormore vector ratios based on the vector parameters to monitor changes inthe patient signal data waveform.

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the followingdetailed description. It is not intended to identify features oressential features of the claimed subject matter, nor is it intendedthat it be used to limit the scope of the claimed subject matter.Furthermore, the claimed subject matter is not limited toimplementations that solve any or all disadvantages noted in any part ofthis disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of the present disclosure and many of theattendant aspects thereof will be readily obtained as the same becomesbetter understood by reference to the following detailed descriptionwhen considered in connection with the accompanying drawings.Furthermore, it should be noted that the same numbers are usedthroughout the drawings to reference like elements and features.

FIG. 1 illustrates an exemplary signal vector system;

FIG. 2 illustrates an exemplary vector circle-based segmentation;

FIG. 3 shows exemplary vector time durations;

FIG. 4 shows an exemplary system;

FIG. 5 shows an exemplary method of analyzing patient signals based onvector analysis;

FIG. 6 shows an exemplary artificial neural network (ANN) module formulti-patient data fusion;

FIG. 7 illustrates a computer simulation of a multi-channel cardiacsignal diagnostic application; and

FIG. 8 shows a table that includes calculation results based on thecomputer simulation.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forthsuch as examples of specific components, devices, methods, etc., inorder to provide a thorough understanding of embodiments of the presentinvention. It will be apparent, however, to one skilled in the art thatthese specific details need not be employed to practice embodiments ofthe present invention. In other instances, well-known materials ormethods have not been described in detail in order to avoidunnecessarily obscuring embodiments of the present invention. While theinvention is susceptible to various modifications and alternative forms,specific embodiments thereof are shown by way of example in the drawingsand will herein be described in detail. It should be understood,however, that there is no intent to limit the invention to theparticular forms disclosed, but on the contrary, the invention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention.

It is to be understood that the system and methods described herein maybe implemented in various forms of hardware, software, firmware, specialpurpose processors, or a combination thereof. Preferably, the presentinvention is implemented in software as an application (e.g., n-tierapplication) comprising program instructions that are tangibly embodiedon one or more program storage devices (e.g., magnetic floppy disk, RAM,CD ROM, ROM, etc.), and executable by any device or machine comprisingsuitable architecture. If written in a programming language conformingto a recognized standard, sequences of instructions designed toimplement the methods can be compiled for execution on a variety ofhardware platforms and for interface to a variety of operating systems.In addition, embodiments of the present framework are not described withreference to any particular programming language. It will be appreciatedthat a variety of programming languages may be used to implementembodiments of the present invention.

It is to be further understood that since the constituent system modulesand method steps depicted in the accompanying Figures are preferablyimplemented in software, the actual connections between the systemcomponents (or the flow of the process steps) may differ depending uponthe manner in which the present invention is programmed. Given theteachings herein, one of ordinary skill in the related art will be ableto contemplate these and similar implementations or configurations ofthe present invention.

The present framework provides a methodology to analyze patient signals.In accordance with one aspect, the present framework analyzeselectrophysiological function signals (e.g., surface ECG signals, ICEGsignals, etc.) based on region of interest (ROI) signal portion shapeand morphology changes. Such analysis may use dynamic signal vectorparameter values, vector ratios and statistical distribution of the ROIsignal waveform morphologies to characterize minute signal datavariation. By providing qualitative and quantitative evaluation ofchange ratios of different portions of the signal waveform, includingthe use of unilateral and/or bilateral ROI signal vector ratios ofdifferent signal portions, the accurate time stamp, location, type andseverity of cardiac myocardial pathology and clinical events may be moreprecisely and reliably diagnosed, detected, mapped and characterized.The present framework advantageously provides a more efficient, accurateand reliable method for identifying cardiac function disorders,differentiating cardiac arrhythmias, characterizing heart pathologicalseverities and tissue location, predicting life-threatening events,and/or evaluating the drug delivery and effects.

FIG. 1 illustrates an exemplary ROI signal vector system. The ROI signalvector system is generated based on a waveform 102. Waveform 102 is atypical waveform that may be generated from an electrophysiologicalsignal (e.g., surface or intra-cardiac ECG). The waveform 102 reflectsthe electrical activity of the heart, with time represented on thehorizontal axis (or x axis) and voltage represented on the vertical axis(or y axis). The waveform 102 may be categorized or segmented by severaldifferent ways, such as signal peak timing, signal amplitude range, etc.In accordance with some implementations, the whole cycle of theelectrophysiological signal is divided by ROI signal vectors, distancecircles of vectors and/or vector time durations.

In some implementations, the signal vector system is centered at acommon R wave peak point 104 of the waveform 102. Different vectors(e.g., R{right arrow over (H)}, R{right arrow over (P)}, R{right arrowover (Q)}, R{right arrow over (S)}, R{right arrow over (T)} and R{rightarrow over (U)}) originating from the R wave peak 104 to points ofinterest on the waveform 102 may be defined. Points of interest mayinclude P, Q, S, T, U wave peak points and an H point of the waveform102. The H point may be defined at a half cardiac cycle time point ofthe waveform 102 measured from the R peak time, and can be used todetermine the resting stage vector R{right arrow over (H)}. Clinically,these vectors represent the energy and amplitude differences and flows,which can be used to calculate waveform distribution changes due tocardiac arrhythmia or pathology events. It should be appreciated thatthe signal vector system may also be centered at a common point otherthan the R wave peak 104, and similar vectors originating from suchcommon center to other points of interest may also be defined. Forexample, software or clinical users may define similar signal vectorsand associated ratios based on the application and clinical needs, suchas T wave centered vectors: T{right arrow over (H)}, T{right arrow over(P)}, and etc. Other peaks of the waveform 102 may also be used as acenter point for vector creation.

By using such vector definition and segmentation, the whole cardiaccycle may be further categorized by vector circles. In accordance withsome implementations, a mapping based on vector circles is provided togenerate a two-dimensional visualization of the signal amplitude range,energy size, cardiac vector scanning area, etc. FIG. 2 illustrates anexemplary vector circle-based segmentation. For purposes ofillustration, two vector circles centered at an R wave peak are shown:Circle(R{right arrow over (H)}) 202 and Circle(R{right arrow over (S)}))204. However, it should be appreciated that other number and/or types ofvector circles may also be defined based on other points of interest.Different vectors and vector circles may be utilized to qualitativelyillustrate and quantitatively calculate minute signal changes anddistortion due to cardiac arrhythmia and medical events.

Each vector circle may be centered at the R wave peak (or other vectorcenter points) with a radius defined by one of the vectors. In thisexample, the radius of the vector circle Circle(R{right arrow over (H)})202 is defined by the R{right arrow over (H)} vector, while the radiusof the vector circle Circle(R{right arrow over (S)}) is defined by theR{right arrow over (S)} vector. It can be determined in this examplethat Circle(R{right arrow over (H)}) 202 is the biggest circle andcovers the entire cardiac cycle. Other vector circles centered at the Rwave peak may also be defined based on other vectors (e.g., R{rightarrow over (P)}, R{right arrow over (Q)}, R{right arrow over (T)} orR{right arrow over (U)}). Accordingly, one-dimensional vectors may bemapped to their respective two-dimensional circles. The shapes, areas orsizes of the vector circles may then be compared to calculate thequantitative energy-amplitude and timing difference for characterizingcardiac functionality.

Vector time durations may further be defined to more efficiently analyzecardiac waveform changes. FIG. 3 shows exemplary vector time durations.These vector time durations may be integrated into cardiac waveformvector analysis and used for monitoring cardiac ROI timing. As shown,the time durations (e.g., T_(HP), T_(HQ), T_(HR), T_(H′R), T_(H′S),T_(H′T), T_(H′U), etc.) are measured from H or H′ to the time stamps ofthe points of interest (e.g., P, Q, R, S, T, U peak points). H or H′ aretwo half cycle time stamps from the R wave peak timing. H and H′ may bederived by using RR wave detection. Accordingly, by using the timecalibration of H and H′ points, different vector time durations may bedefined. These timing parameters correspond to the R wave signal vectors(e.g., R{right arrow over (H)}, R{right arrow over (P)}, R{right arrowover (Q)}, R{right arrow over (S)}, R{right arrow over (T)} and R{rightarrow over (U)}) and can be used for cardiac functionalitycharacterization.

FIG. 4 shows an exemplary system 400 for implementing a method andsystem of the present disclosure. It is to be understood that, becausesome of the constituent system components and method steps depicted inthe accompanying figures can be implemented in software, the actualconnections between the systems components (or the process steps) maydiffer depending upon the manner in which the present framework isprogrammed. For example, the system 400 may be implemented in aclient-server, peer-to-peer (P2P) or master/slave configuration. In suchconfigurations, the system 400 may be communicatively coupled to othersystems or components via a network, such as an Intranet, a local areanetwork (LAN), a wide area network (WAN), a P2P network, a globalcomputer network (e.g., Internet), a wireless communications network, orany combination thereof. Given the teachings of the present inventionprovided herein, one of ordinary skill in the related art will be ableto contemplate these and similar implementations or configurations ofthe present invention.

As shown in FIG. 4, the system 400 may include a computer system 401, apatient monitor 430 and a medical treatment device 432. In someimplementations, the computer system 401 is implemented as animplantable cardiac pacing control device (ICD), as will be discussed inmore detail later. Other implementations, such as a server, desktopcomputer, mobile computing device, laptop, tablet, etc., are alsopossible. The computer system 401 may include, inter alia, a centralprocessing unit (CPU) 402, a non-transitory computer-readable media 405,one or more output devices 411 (e.g., printer, display monitor,projector, speaker, etc.), a network controller 403, an internal bus 406and one or more input devices 408, for example, a keyboard, mouse, touchscreen, gesture and/or voice recognition module, etc. Computer system401 may further include support circuits such as a cache, a powersupply, clock circuits and a communications bus. Various otherperipheral devices, such as additional data storage devices and printingdevices, may also be connected to the computer system 401.

The present technology may be implemented in various forms of hardware,software, firmware, special purpose processors, or a combinationthereof, either as part of the microinstruction code or as part of anapplication program or software product, or a combination thereof, whichis executed via the operating system. In one implementation, thetechniques described herein may be implemented as computer-readableprogram code tangibly embodied in non-transitory computer-readable media405. Non-transitory computer-readable media 405 may include randomaccess memory (RAM), read only memory (ROM), magnetic floppy disk, flashmemory, and other types of memories, or a combination thereof. Thepresent techniques may be implemented by patient signal analysis unit422 that is stored in computer-readable media 405. As such, the computersystem 401 is a general-purpose computer system that becomes aspecific-purpose computer system when executing the computer-readableprogram code.

The same or different computer-readable media 405 may be used forstoring a database 424. Database 424 may include a repository ofdetermined parameters, indices and/or ratios, selectable predeterminedfunctions, patient signal data (e.g., electrophysiological, ECG, ICEG,respiration signal data, other hemodynamic or vital sign data, etc.),patient data (e.g., demographic data, pathology history, etc.), otherinput data and/or other derived output parameters. Patient signal datamay be provided by a patient monitor 430 that is communicatively coupledto the computer system 401.

Patient monitor 430 may be used to acquire various types of patientbiometric or electrphysiological signal information for monitoring thepatient. For example, the monitoring information may include, but is notlimited to, electrophysiological signal data (e.g., ECG, ICEG, etc.),oximetric or SPO2 signal data, respiration signal data, blood pressure,temperature and/or other patient biometric, physiological, hemodynamic,vital sign or medical parameter information. The patient monitor 430 mayinclude appropriate biometric sensors (e.g., leads for surface ECG andbasket catheter for intra-cardiac electrographic signal data) foracquiring the monitoring patient signals. Implementations of the presentframework provide parameters and/or indices to detect, diagnose andquantify such patient signals.

Medical treatment device 432 may be automatically and adaptivelycontrolled by the computer system 401 in a closed-loop feedback controlsystem. Medical treatment device 432 may include, but are not limitedto, a pacing device, ablator, cardioverter, defibrillator, and so forth.Control parameters of the medical treatment device 432, such as thepacing parameter, ablation energy control, etc., may be automaticallydetermined by computer system 401.

FIG. 5 shows an exemplary method 500 of analyzing patient signals basedon vector analysis. The steps of the method 500 may be performed in theorder shown or a different order. Additional, different, or fewer stepsmay be provided. Further, the method 500 may be implemented with thesystem 400 of FIG. 4, a different system, or a combination thereof.

At 502, patient monitor 430 acquires patient signal data from a currentpatient. In some implementations, the patient signal data comprisescardiac electrophysiological signal data, such as intra-cardiacelectrographic (ICEG) data, surface ECG data, etc. The patient signaldata may be represented by a waveform or graph, with time represented onthe x-axis and voltage represented on the y-axis. The cardiacelectrophysiological signal data may be acquired by multiple channelsconnected to an intra-cardiac basket catheter placed into, for example,the right atrium of the heart. Alternatively, or additionally, othertypes of electrophysiological signal data, such as hemodynamic (HEMO)signal data (e.g., invasive blood pressure, non-invasive blood pressuresignal data, cardiac output signals, etc.), respiration (orcapnographic) signal data, blood pressure data, oximetric (SPO2) data,and/or other vital sign signal data, other measurable patient biometric,physiological or medical signals, may also be acquired. In addition,other patient information, such as demographic data, clinicalapplication and patient status, including, but not limited to, weight,height, gender, age, allergies, medications, pathology history,pathology treatment history, etc., may also be acquired.

At 504, the patient signal data is pre-processed. The patient signaldata may be pre-processed by conditioning, filtering, amplification,digitization and/or buffering. For example, the patient signal data maybe pre-filtered and amplified for display on, for instance, patientmonitor 430. The patient signal data may be filtered to remove unwantedpatient movement and respiratory artifacts, as well as power line noise.The filter may be adaptively selected in response to data indicatingclinical application (e.g. ischemia detection application, rhythmanalysis application). The patient signal data may be conditioned,amplified, buffered, filtered and/or digitized to produce a continuousstream of digitized samples.

At 506, patient signal analysis unit 422 may determine whether abaseline value or signal is to be automatically extracted from thedigitized patient signal data. The baseline value (or signal) generallyrefers to a known threshold value (or benign signal) with which anunknown value (e.g., amplitude) is compared when measured or assessed.The baseline value may be used in, for example, threshold determination,computation of parameters or indices, and so forth.

If the baseline value or signal is to be automatically determined, at508, patient signal analysis unit 422 automatically generates thebaseline cardiac value or signal. The baseline value may comprise a zerovoltage line if a static (DC) voltage signal component is filtered outfrom the signal. The baseline value may be adaptively adjusted accordingto the current application and clinical requirements. Alternatively, ifthe value is not to be automatically determined, the user may manuallyselect it via, for example, a user interface.

At 510, patient signal analysis unit 422 determines continuous cardiaccycles. Continuous cardiac cycles may be determined by, for example, Rwave detection using an amplitude threshold for R waves.

At 512, patient signal analysis unit 422 performs segmentation of thewaveform of the patient signal data. The segmentation may be performedwithin a region of interest in the waveform. The region of interest(ROI) may be any portion of the waveform that is identified for furtheranalysis.

The segmentation is performed to generate vectors that are directed froma common center (or origin) to points of interest on the waveform of thepatient signal data, as previously described with reference to FIG. 1.The segmentation is performed by first detecting a vector center in thecontinuous cardiac cycles. The vector center serves as the point fromwhich the vectors originate. In some implementations, the vector centeris the R wave peak point. The R wave is also known as the intrinsicoiddeflection, which represents the time taken for excitation to spreadfrom the endocardial to the epicardial surface of the left ventricle ofthe heart. Other points of interest on the waveform, such as the H, P,Q, S, T and U key points, may also be detected. Such points of interestmay be determined by, for example, a peak detector. After the center andpoints of interest are detected, vectors (e.g., R{right arrow over (H)},R{right arrow over (P)}, R{right arrow over (Q)}, R{right arrow over(S)}, R{right arrow over (T)} and R{right arrow over (U)}) may bedefined from the center to the points of interest.

At 514, patient signal analysis unit 422 extracts one or more vectorparameters from the generated vectors. The one or more vector parametersmay include any parameters that may be defined and measured based on thevector segmented waveform. Types of vector parameters include, but arenot limited to, vector circles centered at a common center and radiusdefined by one of the generated vectors (such as those previouslydescribed with respect to FIG. 2) or vector time durations measured froma half cycle time stamp to time stamps of the points of interest (suchas those previously described with respect to FIG. 3).

At 516, patient signal analysis unit 422 determines if ROI signal andratio selection is to be performed. Depending on the clinicalapplication and/or user experience, ROI signal and ratio selection maybe performed. If ROI signal and ratio selection is not to be performed,the method 500 returns to step 514. If yes, the method 500 proceeds tothe next step 518.

At 518, patient signal analysis unit 422 determines one or more vectorratios and indices based on the vector parameters to monitor changes inthe patient signal data waveform. Vector ratios may include, but are notlimited to, distance ratios, time duration ratios, amplitude to timeduration ratios (or time duration to amplitude ratios), energy arearatios, energy area to time duration ratios, etc. Vector indices mayinclude, but are not limited to, unilateral and bilateral vector ratioindices, as well as statistical indices.

In some implementations, a distance ratio that compares distances ofdifferent vectors is determined as follows:

$\begin{matrix}{{Vector\_ Ratio}_{{{ROI}\; 1} - {{ROI}\; 2}} = {\frac{{ROI}\; 1{\_ Vector}}{{ROI}\; 2{\_ Vector}}}} & (1)\end{matrix}$wherein Vector_Ratio_(ROI1-ROI2) is the ratio of distances associatedwith two different corresponding vectors ROI1_Vector and ROI2_Vectororiginating from a common center on the waveform. In someimplementations, ROI1_Vector and ROI2_Vector are any two differentvectors originating from the R wave peak to different points of interest(e.g., R{right arrow over (H)}, R{right arrow over (P)}, R{right arrowover (Q)}, R{right arrow over (S)}, R{right arrow over (T)} or R{rightarrow over (U)}) on the waveform. The Vector_Ratio_(ROI1-ROI2) may beused to monitor vector distance distortion. For example, the ratio ofR{right arrow over (P)} to R{right arrow over (U)} distances shows thevector distance difference between the P wave and U wave, and isindicative of combination changes of the ROI signal timings and voltageamplitudes, relative to the R wave.

In some implementations, a time duration ratio that compares timedurations of different vectors may be determined as follows:

$\begin{matrix}{{{Vector\_ timing}{\_ Ratio}_{{{ROI}\; 1} - {{ROI}\; 2}}} = {\frac{{ROI}\; 1{\_ Vector}{\_ timing}}{{ROI}\; 2{\_ Vector}{\_ timing}}}} & (2)\end{matrix}$wherein Vector_ti min g_Ratio_(ROI1-ROI2) is the ratio of time durationsassociated with two different vectors originating from the same center(e.g., R wave peak). Such time durations may be selected from the set oftime durations: T_(HP), T_(HQ), T_(HR), T_(H′R), T_(H′S), T_(H′T), andT_(H′U), as previously described with reference to FIG. 3. The Vector_timin g_Ratio_(ROI1-ROI2) shows the difference or latency change betweenthe respective vector time durations. For example, the time durationratio of T_(HP) to T_(HQ) is indicative of the combination change of theROI signal timing and latency, particularly the dynamic changes in the Pwave portion or the atrial electrophysiological activity.

In some implementations, a vector amplitude to time duration ratioVector_Amp_Time_Ratio_(ROI1-ROI2) is determined as follows:

$\begin{matrix}{{{Vector\_ Amp}{\_ Time}{\_ Ratio}_{{{ROI}\; 1} - {{ROI}\; 2}}} = {\frac{{ROI}\; 1{\_ Vector}{\_ Amp}}{{ROI}\; 2{\_ Vector}{\_ Time}}}} & (3)\end{matrix}$Alternatively, or additionally, a vector time duration to amplituderatio Vector_Time_Amp_Ratio_(ROI1-ROI2) may be determined as follows:

$\begin{matrix}{{{Vector\_ Time}{\_ Amp}{\_ Ratio}_{{{ROI}\; 1} - {{ROI}\; 2}}} = {\frac{{ROI}\; 1{\_ Vector}{\_ Time}}{{ROI}\; 2{\_ Vector}{\_ Amp}}}} & (4)\end{matrix}$wherein ROIi_Vector_Amp denotes the amplitude (i.e., distance or size)of vector ROI_vector, in the cardiac ROI signal portion andROIi_Vector_Amp=|ROI_vector_(i)|, and i=1 or 2. The vector ROI vector,may be any one of these vectors: R{right arrow over (H)}, R{right arrowover (P)}, R{right arrow over (Q)}, R{right arrow over (S)}, R{rightarrow over (T)}, and R{right arrow over (U)}. ROIi_Vector_Time denotesthe ROI signal portion vector time duration, which may be any one of thetiming durations: T_(HP), T_(HQ), T_(HR), T_(H′R), T_(H′S), T_(H′T), andT_(H′U).

The cardiac signal amplitudes and time durations may be extracted fromdifferent ROI signal vectors, in which case the ratio is referred to asa “cross vector ratio.” Alternatively, the cardiac signal amplitudes andtime durations are extracted from the same ROI signal vector, in whichcase the ratio is referred to as a “uni-vector ratio.” The cardiac ROIsignal vector amplitude to time duration (or the vector time duration toamplitude ratio) is indicative of the change of speed of the signalwaveform, which is also the energy charging or discharging speed fordifferent stages of the myocardial electrophysiological dynamicactivities. This ratio may be utilized for tracking cardiacfunctionality distortion, especially heart rate change in the signalamplitude.

In some implementations, a vector energy ratio is determined as follows:

$\begin{matrix}{{{Vector\_ Energy}{\_ area}{\_ Ratio}_{{{ROI}\; 1} - {{ROI}\; 2}}} = {\frac{S\left( {{ROI}\; 1{\_ map}{\_ circle}} \right)}{S\left( {{ROI}\; 2{\_ map}{\_ circle}} \right)}}} & (5)\end{matrix}$wherein Vector_Energy_area_Ratio_(ROI1-ROI2) is the ROI signal vectorenergy area ratio between vectors ROI1 and ROI2; S(•) is the functionfor calculating area, S(ROI1_map_circle) is the total area of the ROI1vector circle and S(ROI2_map_circle) is the total area of the ROI2vector circle. Exemplary vector circles were previously described withreference to FIG. 2. By using the ratio of the two vector circle areas,the energy difference and minute dynamic distortion due to cardiacpathologies and events may be characterized.

In some implementations, a vector energy area to time duration ratio isdetermined to compare the ROI signal vector energy with corresponding orother ROI signal vector time duration as follows:

$\begin{matrix}{{{Vector\_ Energy}{\_ area}{\_ to}{\_ time}{\_ Ratio}_{{{ROI}\; 1} - {{ROI}\; 2}}} = {\frac{S\left( {{ROI}\; 1{\_ map}{\_ circle}} \right)}{{ROI}\; 2{\_ Vector}{\_ timing}}}} & (6)\end{matrix}$wherein Vector Energy area to time Ratio_(ROI1-ROI2) is the ratio of ROIsignal vector energy area associated with the vector circle of vectorROI1 to time duration associated with vector ROI2; S(•) is the functionfor calculating area; S(ROI1_map_circle) is the total area of the ROI1vector circle; and ROI2_Vector_ti min g may be one of the timedurations: T_(HP), T_(HQ), T_(HR), T_(H′R), T_(H′S), T_(H′T), andT_(H′U). By using the vector circle area to corresponding vector timeduration ratio, the instant (timing based) energy difference and minutedynamic distortion due to cardiac pathologies and events for specificcardiac electrophysiological stages can be captured and determined.

In the section above, different ratios and parameters are provided.These ratios and parameters may be used to track, detect andcharacterize the ROI or whole cardiac signal waveform changes in asingle heart cycle, such as comparing the RT vector to RP vector, etc.The different ratios between different ROI portions within the samecardiac heart cycle are referred to herein as mutual or cross comparisonvector signal ratios. ROI vectors from different cardiac cycles orepisodes, such as healthy portion and signal portions of interest, mayalso be compared. In order to more efficiently illustrate minute signalchanges in different portions, the different ratios may be integrated asunilateral or bilateral vector ratios. A unilateral vector ratio isbased on the vector ratios of the same ROI vector portion and parameterwithin different heart cycles; while bilateral vector ratio index isbased on the vector ratios of different ROI vector portions andparameters within different heart cycles.

Accordingly, exemplary unilateral ratios may be defined as follows:

$\begin{matrix}{{{Vector\_ Ratio}_{{{ROI}\; 1} - {{ROI}\; 2}}\left( {i,j} \right)} = {\frac{{ROI}\; 1{\_ Vector}(i)}{{ROI}\; 2{\_ Vector}(j)}}} & (7) \\{{{Vector\_ timing}{\_ Ratio}_{{{ROI}\; 1} - {{ROI}\; 2}}\left( {i,j} \right)} = {\frac{{ROI}\; 1{\_ Ve}\;{ctor\_ timing}(i)}{{ROI}\; 2{\_ Vector}{\_ timing}(j)}}} & (8) \\{{{Vector\_ Amp}{\_ Time}{\_ Ratio}_{{{ROI}\; 1} - {{ROI}\; 2}}\left( {i,j} \right)} = {\frac{{ROI}\; 1{\_ Ve}\;{ctor\_ Amp}(i)}{{ROI}\; 2{\_ Vector}{\_ Time}(j)}}} & (9) \\{{{Vector\_ Energy}{\_ area}{\_ Ratio}_{{{ROI}\; 1} - {{ROI}\; 2}}\left( {i,j} \right)} = {\frac{S\left( {{{ROI}\; 1{\_ map}{\_ circle}},i} \right)}{S\left( {{{ROI}\; 2{\_ map}{\_ circle}},j} \right)}}} & (10) \\{{{Vector\_ Energy}{\_ area}{\_ to}{\_ time}{\_ Ratio}_{{{ROI}\; 1} - {{ROI}\; 2}}\left( {i,j} \right)} = {\frac{S\left( {{{ROI}\; 1{\_ map}{\_ circle}},i} \right)}{{ROI}\; 2{\_ Vector}{\_ timing}(j)}}} & (11)\end{matrix}$wherein i and j denote first and second cardiac cycles, and ROI1 andROI2 denote first and second ROI vector portions. If i=j, it means thatcross or mutual vector signal ratios are being determined. If i isdifferent from j but ROI1=ROI2, it means that unilateral cardiac vectorsignal ratios are being determined. If i is different from j and ROI1 isalso different from ROI2, it means that bilateral cardiac vector signalratios are being determined.

The aforementioned parameters and ratios are not only useful for singlelead or channel cardiac electrophysiological signal, but also forcardiac signals from multiple leads or channels, which may includeaveraged or filtered signals or multi-heartbeat averaged cardiac cyclesignals. For different channels or parameter-based signal vector ratioanalysis, statistical analysis and information fusion may be used toobtain a more accurate, reliable and sensitive cardiac functiondiagnosis and pathology detection and characterization.

In some implementations, statistical indices are determined based on thegenerated ratios as follows:

$\begin{matrix}{{{Vector}\mspace{14mu}{ratio}\mspace{14mu}{Mean}\mspace{14mu}{or}\mspace{14mu}{Average}\mspace{14mu}{value}\mspace{11mu}({expectation})\;\text{:}\mspace{14mu}{{mean}(X)}} = {\frac{1}{W}{\sum\limits_{i \in W}{X(i)}}}} & (12) \\{{{Vector}\mspace{14mu}{ratio}\mspace{14mu}{Standard}\mspace{14mu}{deviation}\text{:}\mspace{14mu}{{STD}(X)}} = {\frac{1}{W - 1}{\sum\limits_{i \in {W - 1}}\left( {{X(i)} - {{mean}(X)}} \right)}}} & (13) \\{{{Signal}\mspace{14mu}{Vector}\mspace{14mu}{Ratio}\mspace{14mu}{Variation}\text{:}\mspace{14mu}{{Var}(X)}} = \frac{{mean}(X)}{{STD}(X)}} & (14) \\{{{Signal}\mspace{14mu}{Vector}\mspace{14mu}{Ratio}\mspace{14mu}{Variability}\text{:}\mspace{14mu}{{Var}{\_ b}}} = \frac{\max\left( {X - {{mean}(X)}} \right)}{{mean}(X)}} & (15)\end{matrix}$wherein X is the previously determined vector parameter or ratio (e.g.,vector ratio, unilateral or bilateral ratios derived from vectorparameters or ratios, etc.); and W is the calculation window size (i.e.,there are W heartbeat cycles in a calculation window; heart cycle can bealso derived from cardiac vector signals). In some implementations, thestatistical indices may further include values derived by high orderstatistical calculation (HOS), tests methods (such as t-test) and/orhypothesis evaluations of the signals/data distributions.

At 520, patient signal analysis unit 422 determines if pathology orcardiac event detection is to be performed. If not, the method 500returns to step 510. If yes, the method 500 proceeds to next step 522.

At 522, patient signal analysis unit 422 maps the generated vectorparameters, ratios and/or indices to corresponding cardiac events orpathology (e.g., location, type, severity, trend, etc.). In most cases,the generated vector parameters, ratios and/or indices can provide goodsensitivity and stability for diagnosing cardiac tissue andelectrophysiological-hemodynamic malfunctions. However, the accuracy andreliability may be improved by combining all available patient data,such as other ECG signal data and derived parameters, non-invasive bloodpressure (NIBP) or invasive blood pressure (IBP) signal data, etc.

In some implementations, an artificial neural network (ANN) is used fornonlinear fusion of different types of patient data, including thevector parameters, ratios and/or indices generated by the presentframework. FIG. 6 shows an exemplary ANN module 602 for multi-patientdata fusion. There are 3 layers in the ANN module 602: input layer,hidden layer and output layer. A_(ij) denote weights between the inputlayer and the hidden layer, while B_(pq) denote weights between thehidden layer and the output layer. The weights A_(ij) and B_(pq) can beadaptively adjusted with training data set.

The ANN module 602 has self-learning capability with new input data 604,which can increase the accuracy of calculated results 606. The ANNmodule 602 combines typical signal parameters and vital sign data 604 a,patient signal analysis results (e.g., vector parameters, energy ratios,statistical indices, etc.) 604 b generated by the present framework, andpatient data, history and doctor's knowledge 604 c to generate outputresults 606 for detecting and treating emerging pathological events.More detailed patient status and treatment parameters 606 can bederived, via the ANN module 602, for achieving optimized cardiac rhythmmanagement (CRM). Exemplary output parameters 606 include, but are notlimited to, cardiac arrhythmia type, severity, location, time stamp,event trend, treatment parameter and suggestions (e.g., treatmentlocation, priority, treatment control parameters, etc.). By usingmulti-channel signal data and multiple kinds of patient data, cardiacarrhythmia can be more efficiently detected and characterized. Forinstance, cardiac disorders and arrhythmias may be differentiated,pathological severities may be characterized, life-threatening eventsmay be predicted, and drug delivery and effects may be evaluated.

At 524, patient signal analysis unit 422 may optionally adaptivelyadjust calculation parameters used for calculating the afore-mentionedvector parameters, ratios and/or indices. The adaptive adjustment may beperformed automatically, semi-automatically or manually by the clinicaluser based on clinical experience and knowledge. Such calculationparameters may include, but are not limited to, calculation window size,signal portion, ROI area, time steps, severity thresholds, and so forth.

At 526, patient signal analysis unit 422 generates a patient report. Thepatient report may record the abnormality, associated characteristics(e.g., location, type, severity, timing, etc.) and other information(e.g., suggested treatment options). The patient report may be in theform of, for example, an alert message presented at patient monitor 430.The patient report may also be stored in database 424 for futureretrieval, transmitted or shared with other client computers, and/orprinted in physical form for viewing.

There may be many types of clinical applications that can employ thevector ratios, parameters and/or indices generated by the presentframework. FIG. 7 illustrates a computer simulation of an exemplarymulti-channel cardiac signal diagnostic application. The exemplaryapplication may be implemented as, for example, an implantable cardiacpacing control device (ICD) 702. Based on the myocardial ischemiaseverity and level, appropriate medical treatment may be applied toefficiently save the life of the patient and reduce fatality risk fromcardiac arrhythmia and pathologies.

The ICD equipment 702 may include, for example, the computer system 401to implement the techniques described herein. The ICD equipment 702 mayperform cardiac ROI signal vector analysis to generate vector parametersand associated ratios and/or indices, as well as traditionalintra-cardiac signal analysis, such as ST segment voltage elevation. Insome implementations, the ICD equipment 702 is coupled to anelectrocardiogram recorder with multi-channel sensors and leads 704 toacquire an ongoing cardiac electrophysiological signal. As shown, threedifferent leads (or positions P1, P2 and P3) may be provided to localizethe cardiac arrhythmia mapping of the ventricular myocardial tissue.

The ICD equipment 702 may map the acquired signal data and generatedvector parameters, ratios and/or indices to pathology characteristics(e.g., location, severity, treatment priorities, etc.) and treatmentcontrol parameters. The ICD device 702 may further be coupled to acardiac ablation or pacing device 705 for treating or correcting heartrhythm problems. The cardiac ablation or pacing device 705 may be pairedwith the multi-channel sensors and leads 704, and controlled by thegenerated treatment control parameters to provide real-time treatmentthat is adaptive based on local signal data acquired from neighboringsites. For example, the ICD device 702 may adjust the size or durationof pacing energy, sequence of ablation, etc., according to the locallyacquired signal data and associated treatment control parameters.

In order to differentiate myocardial ischemia events, three differentlevels of myocardial ischemia severity were determined: (1) normalcardiac rhythm, (2) early ischemia and (3) early infarction (lateischemia). FIG. 8 shows a table 802 that includes the calculationresults based on the computer simulation. For purposes of illustration,the vector energy ratio Vector_Energy_area_Ratio_(RP-RT) was determined.As can be observed, position P2 showed more signal distortions thanpositions P1 and P3, which means position P2 is determined as thehighest priority cardiac site for electrical shock and ablation. For allpositions along the catheter for ICD equipment 702, continuousmonitoring of different positions (e.g., P1, P2, P3, etc.) greatlyfacilitates real-time diagnosis and treatment. Especially for P2, normalstandard clinical methods (ST segment elevation voltage changes) is 0.01mV for early ischemia event and 0.08 mV for late ischemia, which wouldnot have crossed the standard 0.1 mV threshold to generate a warning.The ROI vector signal energy area ratio(Vector_Energy_area_Ratio_(RP-RT)) showed more than 100% change in valuefor early ischemia and more than 200% change in value for late ischemia.This indicates that the signal portion vector ratio analysis may greatlyhelp more sensitive and reliable detection of ventricular ischemiaevents. Different kinds of tests and probability analysis may further beused to find the specific type of cardiac arrhythmia.

In summary, multi-channel signal ROI vector analysis advantageouslyprovides an efficient approach for cardiac function mapping, diagnosingseverity-location-type of the pathology, predicting cardiac eventtrends, and providing suggestions for effective treatment and priorityworkflow. Additionally, multi-channel signal vector parameters, ratiosand/or indices can be mapped to a two-dimensional or three-dimensionalvisual representation of the heart. Furthermore, multi-dimensionalsignal vector ratio information mapping may be used in real-time cardiacfunction diagnosis (signal vector ratio mode vs. time). By usingmulti-channel signal vector energy distribution information mapping, theabnormal tissue location, potential abnormal pathway, arrhythmiaseverity, etc., may be visually mapped and predicted, which providesinformative feedback to the clinical user for providing additionaltreatment and drug delivery. The present techniques of cardiac signalenergy mode estimation and patient healthy status characterization canbe safely and easily implemented in pacemakers and cardiac implantabledevices for characterizing and treating patient cardiac pathology andarrhythmia by using, for example, ICEG signals.

While the present invention has been described in detail with referenceto exemplary embodiments, those skilled in the art will appreciate thatvarious modifications and substitutions can be made thereto withoutdeparting from the spirit and scope of the invention as set forth in theappended claims. For example, elements and/or features of differentexemplary embodiments may be combined with each other and/or substitutedfor each other within the scope of this disclosure and appended claims.

The invention claimed is:
 1. A system for patient signal analysis, comprising: a patient monitor that acquires a patient signal data waveform from a patient; a medical treatment device that provides treatment to the patient; and a computer system communicatively coupled to the patient monitor and the medical treatment device, wherein the computer system includes a non-transitory memory device for storing computer readable program code, and a processor in communication with the memory device, the processor being operative with the computer readable program code to perform steps including generating a set of vectors directed from a common center to points of interest on the patient signal data waveform, extracting one or more vector parameters from the vectors, determining one or more vector ratios based on the one or more vector parameters to monitor changes in the patient signal data waveform, wherein determining the one or more vector ratios comprises determining one or more unilateral ratios from the vectors or determining one or more bilateral ratios from the vectors, determining one or more control parameters based on the one or more vector ratios, and controlling the medical treatment device by applying the control parameters.
 2. The system of claim 1 wherein the computer system comprises an implantable cardiac pacing control device.
 3. The system of claim 1 wherein the patient monitor comprises an electrocardiogram recorder with multiple leads for acquiring multi-channel patient signal data.
 4. The system of claim 3 wherein the multiple leads are connected to a intra-cardiac basket catheter.
 5. The system of claim 4 wherein the intra-cardiac basket catheter is placed into the right atrium of the patient's heart.
 6. The system of claim 3 wherein the medical treatment device comprises a cardiac ablation or pacing device.
 7. The system of claim 6 wherein the cardiac ablation or pacing device is paired with the multiple leads and controlled by the control parameters and wherein the medical treatment is in real-time and adaptive based on the acquired patient signal data waveform.
 8. The system of claim 1 wherein the patient signal data waveform comprises an electrocardiogram waveform.
 9. The system of claim 1 wherein the patient signal data waveform comprises one of hemodynamic signal data, respiration signal data, blood pressure data and oximetric data.
 10. The system of claim 1 further comprising an artificial neural network configured to map the one or more vector ratios and patient data to cardiac event or pathology.
 11. The system of claim 1 wherein the artificial neural network has self-learning capability.
 12. A system for patient signal analysis, comprising: a patient monitor that acquires a patient signal data waveform from a patient; a medical treatment device that provides treatment to the patient; and a computer system communicatively coupled to the patient monitor and the medical treatment device, wherein the computer system includes a non-transitory memory device for storing computer readable program code, and a processor in communication with the memory device, the processor being operative with the computer readable program code to perform steps including generating a set of vectors directed from a common center to points of interest on the patient signal data waveform, extracting one or more vector parameters from the vectors, determining one or more vector ratios based on the one or more vector parameters to monitor changes in the patient signal data waveform, wherein the one or more vector ratios are determined based on vector parameters extracted from the vectors within a single cycle of the patient signal data waveform, determining one or more control parameters based on the one or more vector ratios, and controlling the medical treatment device by applying the control parameters. 